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Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition

IAES International Journal of Artificial Intelligence · Vol. 14 No. 5 (2025) · pp. 3816–3826

DOI
10.11591/ijai.v14.i5.pp3816-3826
ISSN
2252-8938, 2089-4872
Published
2025-10-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.

Keywords

Collaboration network of the authors

10 authors · 14 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Al-Ahliyya Amman University
  • World Islamic Sciences and Education University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Areen M. ArabiatAl-Ahliyya Amman University4
Omar AlmomaniWorld Islamic Sciences and Education University3
Amani M. Al-GhraibahAl-Ahliyya Amman University2
Hamza Abu OwidaAffiliation unknown2
Mohammad HassanAffiliation unknown1
Amani Al-GhraibahAffiliation unknown1
Hamza Abu OwidaAl-Ahliyya Amman University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition</dc:title>
	<dc:creator>Arabiat, Areen</dc:creator>
	<dc:creator>Altayeb, Muneera</dc:creator>
	<dc:creator>Salama, Sanaa</dc:creator>
	<dc:subject xml:lang="en-US">Computer engineering ; Image processing ;Pattern Recognition ;Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">AdaBoost; Advance driver assistance systems; Performance metrices; Random forest; Traffic signs</dc:subject>
	<dc:description xml:lang="en-US">Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-10-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27034</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i5.pp3816-3826</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025; 3816-3826</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27034/14722</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Areen Arabiat, Muneera Altayeb, Sanaa Salama</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>